KSU paired amplicon community ordinations
Comparative NMDS plots of 16S and ITS data (EDGE samples excluded), updated to include adonis2 analyses (permANOVA by metadata)
Kelli Feeser
2023-10-02
opts <- options(knitr.kable.NA = "")1 Set-up
1.1 Import clean OTU tables
1.2 Ordinate all grasses and by-host (trying k 1-7)
1.3 QC, stress check and validation of ordinations
ord_checkfit_df = data.frame()
for (name in comm.ord.names.by.grass) {
output = extract.nmds.checkfit(get(name))
ord_checkfit_df = rbind(ord_checkfit_df, output)
}# Ideal stress value is < 0.05 (but k > 5 makes interpretation difficult)
# Higher than 0.2 is poor (risks for false interpretation).
# 0.1 - 0.2 is fair (some distances can be misleading for interpretation).
# 0.05 - 0.1 is good (can be confident in inferences from plot).
# Less than 0.05 is excellent (this can be rare).
ord_checkfit_df$stress<-round(ord_checkfit_df$stress,3)
ord_checkfit_df$nonmetric.fit.r2<-round(ord_checkfit_df$nonmetric.fit.r2,3)
ord_checkfit_df$linear.fit.r2<-round(ord_checkfit_df$linear.fit.r2,3)
# Final choices on k values
## AllG
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[1])
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[2])
# flipping x axis coords for ease of interpretation
# Fun.AllG.k5.ord$points[,1]<-Fun.AllG.k5.ord$points[,1]*-1 # already done
saveRDS(Bac.AllG.k5.ord, file="../processed_data/clean_rds_saves/Bac.AllG.k5.ord.rds")
saveRDS(Fun.AllG.k5.ord, file="../processed_data/clean_rds_saves/Fun.AllG.k5.ord.rds")
## ANGE
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[3])
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[4])
#flip x coord of ITS Fun.ANGE.k5.ord
# Fun.ANGE.k5.ord$points[,1]<-Fun.ANGE.k5.ord$points[,1]*-1 # already done
saveRDS(Bac.ANGE.k5.ord, file="../processed_data/clean_rds_saves/Bac.ANGE.k5.ord.rds")
saveRDS(Fun.ANGE.k5.ord, file="../processed_data/clean_rds_saves/Fun.ANGE.k5.ord.rds")
## BOER
saveRDS(Bac.BOER.k5.ord, file="../processed_data/clean_rds_saves/Bac.BOER.k5.ord.rds")
saveRDS(Fun.BOER.k5.ord, file="../processed_data/clean_rds_saves/Fun.BOER.k5.ord.rds")
## BOGR
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[7])
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[8])
saveRDS(Bac.BOGR.k5.ord, file="../processed_data/clean_rds_saves/Bac.BOGR.k5.ord.rds")
saveRDS(Fun.BOGR.k5.ord, file="../processed_data/clean_rds_saves/Fun.BOGR.k5.ord.rds")
## BUDA
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[9])
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[10])
# flipping y axis coords for ease of interpretation
# Fun.BUDA.k6.ord$points[,2]<-Fun.BUDA.k6.ord$points[,2]*-1 # already done
saveRDS(Bac.BUDA.k6.ord, file="../processed_data/clean_rds_saves/Bac.BUDA.k6.ord.rds")
saveRDS(Fun.BUDA.k6.ord, file="../processed_data/clean_rds_saves/Fun.BUDA.k6.ord.rds")
## SCSC
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[11])
subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[12])
saveRDS(Bac.SCSC.k5.ord, file="../processed_data/clean_rds_saves/Bac.SCSC.k5.ord.rds")
saveRDS(Fun.SCSC.k5.ord, file="../processed_data/clean_rds_saves/Fun.SCSC.k5.ord.rds")
1.4 Set-up metadata
Available metadata:
## [1] "SampleID" "ITS_ID" "sample_name"
## [4] "Replicate" "Site" "Site_Grass"
## [7] "Site.Rep" "Grass" "Grass_Rep"
## [10] "Edge" "SiteName" "MAT_degC"
## [13] "MAP_mm" "CollectionDate" "Latitude"
## [16] "Longitude" "Elevation_m" "Grassland"
## [19] "Gradient" "Bin" "julianday"
## [22] "coll_GDD" "GDD30yr" "GDD3yr"
## [25] "GDD2015" "ppt30yr" "ppt3yr"
## [28] "ppt2015" "soil_moisture" "GWC"
## [31] "perc_moisture" "pH" "phos"
## [34] "ammonium" "nitrate" "SOM"
## [37] "herbivory_perc" "avg_SLA" "avg_SRL"
2 Data overview
All EDGE samples excluded
OTU tables rarefied (16S: 9,048 & ITS: 10,116 seqs/sample
‘Taxa’ are OTUs
AllG (All Grasses)
Bacterial & Archaeal Dataset:
Bac.AllG## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 23156 taxa and 533 samples ]
## sample_data() Sample Data: [ 533 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 23156 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.AllG## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 6749 taxa and 509 samples ]
## sample_data() Sample Data: [ 509 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 6749 taxa by 7 taxonomic ranks ]
ANGE (Andropogon gerardii / Big bluestem)
Bacterial & Archaeal Dataset:
Bac.ANGE## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 14268 taxa and 106 samples ]
## sample_data() Sample Data: [ 106 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 14268 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.ANGE## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2985 taxa and 98 samples ]
## sample_data() Sample Data: [ 98 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 2985 taxa by 7 taxonomic ranks ]
BOER (B. eriopoda / Black grama)
Bacterial & Archaeal Dataset:
Bac.BOER## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 8374 taxa and 84 samples ]
## sample_data() Sample Data: [ 84 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 8374 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.BOER## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1662 taxa and 78 samples ]
## sample_data() Sample Data: [ 78 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 1662 taxa by 7 taxonomic ranks ]
BOGR (B. gracilis / Blue grama)
Bacterial & Archaeal Dataset:
Bac.BOGR## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 10108 taxa and 86 samples ]
## sample_data() Sample Data: [ 86 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 10108 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.BOGR## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2668 taxa and 92 samples ]
## sample_data() Sample Data: [ 92 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 2668 taxa by 7 taxonomic ranks ]
BUDA (Bouteloua dactyloides / Buffalograss)
Bacterial & Archaeal Dataset:
Bac.BUDA## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 11048 taxa and 83 samples ]
## sample_data() Sample Data: [ 83 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 11048 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.BUDA## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2369 taxa and 80 samples ]
## sample_data() Sample Data: [ 80 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 2369 taxa by 7 taxonomic ranks ]
SCSC (Schizachyrium scoparium / Little bluestem)
Bacterial & Archaeal Dataset:
Bac.SCSC## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 17568 taxa and 174 samples ]
## sample_data() Sample Data: [ 174 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 17568 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.SCSC## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 4347 taxa and 161 samples ]
## sample_data() Sample Data: [ 161 samples by 39 sample variables ]
## tax_table() Taxonomy Table: [ 4347 taxa by 7 taxonomic ranks ]
3 NMDS By Grass Host
- ANGE (Andropogon gerardii / Big bluestem)
- SCSC (Schizachyrium scoparium / Little bluestem)
- BOER (B. eriopoda / Black grama)
- BOGR (B. gracilis / Blue grama)
- BUDA (Bouteloua dactyloides / Buffalograss)
NMDS plots of all samples, colored by grass host
knitr::kable((as.data.frame(subset(adonis.out.array_predGrass,
comm==comm.names.by.grass[1])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.AllG.Grass | 4 | 18.6 | 0.132 | 20.12 | 0.001 |
| Bac.AllG.Residual | 528 | 122.2 | 0.868 | ||
| Bac.AllG.Total | 532 | 140.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGrass,
comm==comm.names.by.grass[2])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.AllG.Grass | 4 | 13.2 | 0.064 | 8.67 | 0.001 |
| Fun.AllG.Residual | 504 | 192.5 | 0.936 | ||
| Fun.AllG.Total | 508 | 205.7 | 1.000 |
4 Subset by Grass Host
4.1 Colored by Latitudinal Bin
AllG (All Grasses)
NMDS plots of all samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[1])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.AllG.Bin | 3 | 7.9 | 0.056 | 10.48 | 0.001 |
| Bac.AllG.Residual | 529 | 132.9 | 0.944 | ||
| Bac.AllG.Total | 532 | 140.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[2])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.AllG.Bin | 3 | 11.3 | 0.055 | 9.79 | 0.001 |
| Fun.AllG.Residual | 505 | 194.4 | 0.945 | ||
| Fun.AllG.Total | 508 | 205.7 | 1.000 |
ANGE (Andropogon gerardii / Big bluestem)
NMDS plots of ANGE host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[3])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.ANGE.Bin | 3 | 4.4 | 0.157 | 6.33 | 0.001 |
| Bac.ANGE.Residual | 102 | 23.8 | 0.843 | ||
| Bac.ANGE.Total | 105 | 28.2 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[4])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.ANGE.Bin | 3 | 6.4 | 0.174 | 6.59 | 0.001 |
| Fun.ANGE.Residual | 94 | 30.6 | 0.826 | ||
| Fun.ANGE.Total | 97 | 37.0 | 1.000 |
BOER (B. eriopoda / Black grama)
NMDS plots of BOER host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[5])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BOER.Bin | 3 | 6 | 0.354 | 14.58 | 0.001 |
| Bac.BOER.Residual | 80 | 11 | 0.646 | ||
| Bac.BOER.Total | 83 | 17 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[6])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BOER.Bin | 3 | 5.4 | 0.194 | 5.94 | 0.001 |
| Fun.BOER.Residual | 74 | 22.3 | 0.806 | ||
| Fun.BOER.Total | 77 | 27.7 | 1.000 |
BOGR (B. gracilis / Blue grama)
NMDS plots of BOGR host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[7])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BOGR.Bin | 3 | 2.9 | 0.168 | 5.51 | 0.001 |
| Bac.BOGR.Residual | 82 | 14.3 | 0.832 | ||
| Bac.BOGR.Total | 85 | 17.2 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[8])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BOGR.Bin | 3 | 4.0 | 0.126 | 4.24 | 0.001 |
| Fun.BOGR.Residual | 88 | 27.7 | 0.874 | ||
| Fun.BOGR.Total | 91 | 31.8 | 1.000 |
BUDA (Bouteloua dactyloides / Buffalograss)
NMDS plots of BUDA host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[9])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BUDA.Bin | 3 | 2.6 | 0.171 | 5.45 | 0.001 |
| Bac.BUDA.Residual | 79 | 12.4 | 0.829 | ||
| Bac.BUDA.Total | 82 | 15.0 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[10])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BUDA.Bin | 3 | 4.3 | 0.14 | 4.11 | 0.001 |
| Fun.BUDA.Residual | 76 | 26.5 | 0.86 | ||
| Fun.BUDA.Total | 79 | 30.8 | 1.00 |
SCSC (Schizachyrium scoparium / Little bluestem)
NMDS plots of SCSC host-associated samples, colored by latitudinal bin
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[11])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.SCSC.Bin | 3 | 4.0 | 0.09 | 5.58 | 0.001 |
| Bac.SCSC.Residual | 170 | 40.8 | 0.91 | ||
| Bac.SCSC.Total | 173 | 44.8 | 1.00 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[12])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.SCSC.Bin | 3 | 5.3 | 0.081 | 4.62 | 0.001 |
| Fun.SCSC.Residual | 157 | 60.0 | 0.919 | ||
| Fun.SCSC.Total | 160 | 65.3 | 1.000 |
4.2 Colored by Longitudinal Gradient
AllG (All Grasses)
NMDS plots of all samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[1])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.AllG.Gradient | 2 | 17.5 | 0.124 | 37.57 | 0.001 |
| Bac.AllG.Residual | 530 | 123.3 | 0.876 | ||
| Bac.AllG.Total | 532 | 140.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[2])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.AllG.Gradient | 2 | 10.4 | 0.05 | 13.45 | 0.001 |
| Fun.AllG.Residual | 506 | 195.3 | 0.95 | ||
| Fun.AllG.Total | 508 | 205.7 | 1.00 |
ANGE (Andropogon gerardii / Big bluestem)
NMDS plots of ANGE host-associated samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[3])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.ANGE.Gradient | 1 | 2.8 | 0.1 | 11.62 | 0.001 |
| Bac.ANGE.Residual | 104 | 25.4 | 0.9 | ||
| Bac.ANGE.Total | 105 | 28.2 | 1.0 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[4])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.ANGE.Gradient | 1 | 1.8 | 0.05 | 5.04 | 0.001 |
| Fun.ANGE.Residual | 96 | 35.2 | 0.95 | ||
| Fun.ANGE.Total | 97 | 37.0 | 1.00 |
BOER (B. eriopoda / Black grama)
[NA on stats here - only 1 factor level]
# knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
# comm==comm.names.by.grass[5])))[,1:5], "simple",
# digits =c (0,1,3,2,3),
# align = 'c')
# knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
# comm==comm.names.by.grass[6])))[,1:5], "simple",
# digits =c (0,1,3,2,3),
# align = 'c')BOGR (B. gracilis / Blue grama)
NMDS plots of BOGR host-associated samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[7])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BOGR.Gradient | 2 | 2.6 | 0.152 | 7.45 | 0.001 |
| Bac.BOGR.Residual | 83 | 14.5 | 0.848 | ||
| Bac.BOGR.Total | 85 | 17.2 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[8])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BOGR.Gradient | 2 | 3.5 | 0.109 | 5.46 | 0.001 |
| Fun.BOGR.Residual | 89 | 28.3 | 0.891 | ||
| Fun.BOGR.Total | 91 | 31.8 | 1.000 |
BUDA (Bouteloua dactyloides / Buffalograss)
NMDS plots of BUDA host-associated samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[9])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BUDA.Gradient | 2 | 2.6 | 0.172 | 8.3 | 0.001 |
| Bac.BUDA.Residual | 80 | 12.4 | 0.828 | ||
| Bac.BUDA.Total | 82 | 15.0 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[10])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BUDA.Gradient | 2 | 3.0 | 0.097 | 4.14 | 0.001 |
| Fun.BUDA.Residual | 77 | 27.8 | 0.903 | ||
| Fun.BUDA.Total | 79 | 30.8 | 1.000 |
SCSC (Schizachyrium scoparium / Little bluestem)
NMDS plots of SCSC host-associated samples, colored by longitudinal gradient
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[11])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.SCSC.Gradient | 2 | 5.9 | 0.131 | 12.93 | 0.001 |
| Bac.SCSC.Residual | 171 | 38.9 | 0.869 | ||
| Bac.SCSC.Total | 173 | 44.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[12])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.SCSC.Gradient | 2 | 3.4 | 0.052 | 4.37 | 0.001 |
| Fun.SCSC.Residual | 158 | 61.8 | 0.948 | ||
| Fun.SCSC.Total | 160 | 65.3 | 1.000 |